{"list":"other","total":80,"rows":[{"id":"1810.04805","title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","created":"2018-10-11","cited_by_count":40399},{"id":"2005.14165","title":"Language Models are Few-Shot Learners","created":"2020-05-28","cited_by_count":32419},{"id":"1910.10683","title":"Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer","created":"2019-10-23","cited_by_count":14599},{"id":"1907.11692","title":"RoBERTa: A Robustly Optimized BERT Pretraining Approach","created":"2019-07-26","cited_by_count":13372},{"id":"2307.09288","title":"Llama 2: Open Foundation and Fine-Tuned Chat Models","created":"2023-07-18","cited_by_count":13028},{"id":"1301.3781","title":"Efficient Estimation of Word Representations in Vector Space","created":"2013-01-16","cited_by_count":4375},{"id":"1910.01108","title":"DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter","created":"2019-10-02","cited_by_count":4307},{"id":"1310.4546","title":"Distributed Representations of Words and Phrases and their Compositionality","created":"2013-10-16","cited_by_count":3474},{"id":"2307.15043","title":"Universal and Transferable Adversarial Attacks on Aligned Language Models","created":"2023-07-27","cited_by_count":3104},{"id":"2112.04359","title":"Ethical and social risks of harm from Language Models","created":"2021-12-08","cited_by_count":966},{"id":"1908.07125","title":"Universal Adversarial Triggers for Attacking and Analyzing NLP","created":"2019-08-20","cited_by_count":722},{"id":"2312.02119","title":"Tree of Attacks: Jailbreaking Black-Box LLMs Automatically","created":"2023-12-04","cited_by_count":672},{"id":"2004.09984","title":"BERT-ATTACK: Adversarial Attack Against BERT Using BERT","created":"2020-04-21","cited_by_count":491},{"id":"2101.05783","title":"Persistent Anti-Muslim Bias in Large Language Models","created":"2021-01-14","cited_by_count":377},{"id":"2110.01963","title":"Multimodal datasets: misogyny, pornography, and malignant stereotypes","created":"2021-10-05","cited_by_count":327},{"id":"2011.10369","title":"ONION: A Simple and Effective Defense Against Textual Backdoor Attacks","created":"2020-11-20","cited_by_count":263},{"id":"2105.12400","title":"Hidden Killer: Invisible Textual Backdoor Attacks with Syntactic Trigger","created":"2021-05-26","cited_by_count":222},{"id":"2310.06816","title":"Text Embeddings Reveal (Almost) As Much As Text","created":"2023-10-10","cited_by_count":192},{"id":"2103.14659","title":"Alignment of Language Agents","created":"2021-03-26","cited_by_count":168},{"id":"2403.06634","title":"Stealing Part of a Production Language Model","created":"2024-03-11","cited_by_count":159},{"id":"2110.07139","title":"Mind the Style of Text! Adversarial and Backdoor Attacks Based on Text Style Transfer","created":"2021-10-14","cited_by_count":158},{"id":"2301.04246","title":"Generative Language Models and Automated Influence Operations: Emerging Threats and Potential Mitigations","created":"2023-01-10","cited_by_count":158},{"id":"2009.07502","title":"Contextualized Perturbation for Textual Adversarial Attack","created":"2020-09-16","cited_by_count":136},{"id":"2301.10140","title":"The Semantic Scholar Open Data Platform","created":"2023-01-24","cited_by_count":118},{"id":"2108.13888","title":"Backdoor Attacks on Pre-trained Models by Layerwise Weight Poisoning","created":"2021-08-31","cited_by_count":113},{"id":"2110.07831","title":"RAP: Robustness-Aware Perturbations for Defending against Backdoor Attacks on NLP Models","created":"2021-10-15","cited_by_count":104},{"id":"2305.01219","title":"Prompt as Triggers for Backdoor Attack: Examining the Vulnerability in Language Models","created":"2023-05-02","cited_by_count":94},{"id":"2106.06361","title":"Turn the Combination Lock: Learnable Textual Backdoor Attacks via Word Substitution","created":"2021-06-11","cited_by_count":89},{"id":"2009.09191","title":"OpenAttack: An Open-source Textual Adversarial Attack Toolkit","created":"2020-09-19","cited_by_count":84},{"id":"2305.03010","title":"Sentence Embedding Leaks More Information than You Expect: Generative Embedding Inversion Attack to Recover the Whole Sentence","created":"2023-05-04","cited_by_count":81},{"id":"2406.17092","title":"BEEAR: Embedding-based Adversarial Removal of Safety Backdoors in Instruction-tuned Language Models","created":"2024-06-24","cited_by_count":58},{"id":"2210.09545","title":"Fine-mixing: Mitigating Backdoors in Fine-tuned Language Models","created":"2022-10-18","cited_by_count":57},{"id":"2211.14946","title":"Self-Destructing Models: Increasing the Costs of Harmful Dual Uses of Foundation Models","created":"2022-11-27","cited_by_count":57},{"id":"2210.08523","title":"Some Languages are More Equal than Others: Probing Deeper into the Linguistic Disparity in the NLP World","created":"2022-10-16","cited_by_count":55},{"id":"2104.10097","title":"Beyond Fair Pay: Ethical Implications of NLP Crowdsourcing","created":"2021-04-20","cited_by_count":53},{"id":"2205.12700","title":"BITE: Textual Backdoor Attacks with Iterative Trigger Injection","created":"2022-05-25","cited_by_count":53},{"id":"2005.00174","title":"Universal Adversarial Attacks with Natural Triggers for Text Classification","created":"2020-05-01","cited_by_count":51},{"id":"2205.08305","title":"A Study of the Attention Abnormality in Trojaned BERTs","created":"2022-05-13","cited_by_count":48},{"id":"2305.17826","title":"NOTABLE: Transferable Backdoor Attacks Against Prompt-based NLP Models","created":"2023-05-28","cited_by_count":37},{"id":"2010.06053","title":"TextHide: Tackling Data Privacy in Language Understanding Tasks","created":"2020-10-12","cited_by_count":33},{"id":"2304.14475","title":"ChatGPT as an Attack Tool: Stealthy Textual Backdoor Attack via Blackbox Generative Model Trigger","created":"2023-04-27","cited_by_count":31},{"id":"2105.14553","title":"Defending Pre-trained Language Models from Adversarial Word Substitutions Without Performance Sacrifice","created":"2021-05-30","cited_by_count":30},{"id":"2103.11576","title":"Grey-box Adversarial Attack And Defence For Sentiment Classification","created":"2021-03-22","cited_by_count":28},{"id":"2206.00437","title":"What a Creole Wants, What a Creole Needs","created":"2022-06-01","cited_by_count":27},{"id":"2305.19474","title":"Ethical Considerations for Machine Translation of Indigenous Languages: Giving a Voice to the Speakers","created":"2023-05-31","cited_by_count":27},{"id":"2401.12192","title":"Text Embedding Inversion Security for Multilingual Language Models","created":"2024-01-22","cited_by_count":26},{"id":"2305.02394","title":"Defending against Insertion-based Textual Backdoor Attacks via Attribution","created":"2023-05-03","cited_by_count":25},{"id":"2406.10280","title":"Transferable Embedding Inversion Attack: Uncovering Privacy Risks in Text Embeddings without Model Queries","created":"2024-06-12","cited_by_count":25},{"id":"2205.10710","title":"Phrase-level Textual Adversarial Attack with Label Preservation","created":"2022-05-22","cited_by_count":23},{"id":"2109.04367","title":"Multi-granularity Textual Adversarial Attack with Behavior Cloning","created":"2021-09-09","cited_by_count":21},{"id":"2203.13920","title":"Canary Extraction in Natural Language Understanding Models","created":"2022-03-25","cited_by_count":21},{"id":"2305.11596","title":"Mitigating Backdoor Poisoning Attacks through the Lens of Spurious Correlation","created":"2023-05-19","cited_by_count":21},{"id":"2311.06237","title":"Summon a Demon and Bind it: A Grounded Theory of LLM Red Teaming","created":"2023-11-10","cited_by_count":21},{"id":"2405.11227","title":"BadActs: A Universal Backdoor Defense in the Activation Space","created":"2024-05-18","cited_by_count":21},{"id":"2010.01610","title":"Adversarial Attack and Defense of Structured Prediction Models","created":"2020-10-04","cited_by_count":20},{"id":"2109.10855","title":"BFClass: A Backdoor-free Text Classification Framework","created":"2021-09-22","cited_by_count":19},{"id":"2210.07907","title":"Expose Backdoors on the Way: A Feature-Based Efficient Defense against Textual Backdoor Attacks","created":"2022-10-14","cited_by_count":19},{"id":"2110.08247","title":"Textual Backdoor Attacks Can Be More Harmful via Two Simple Tricks","created":"2021-10-15","cited_by_count":18},{"id":"2106.01452","title":"BERT-Defense: A Probabilistic Model Based on BERT to Combat Cognitively Inspired Orthographic Adversarial Attacks","created":"2021-06-02","cited_by_count":17},{"id":"2205.01094","title":"A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock Predictions","created":"2022-05-01","cited_by_count":15},{"id":"2205.11803","title":"WeDef: Weakly Supervised Backdoor Defense for Text Classification","created":"2022-05-24","cited_by_count":15},{"id":"2305.16503","title":"IMBERT: Making BERT Immune to Insertion-based Backdoor Attacks","created":"2023-05-25","cited_by_count":15},{"id":"2310.18603","title":"Large Language Models Are Better Adversaries: Exploring Generative Clean-Label Backdoor Attacks Against Text Classifiers","created":"2023-10-28","cited_by_count":14},{"id":"2407.05022","title":"A Principled Framework for Evaluating on Typologically Diverse Languages","created":"2024-07-06","cited_by_count":12},{"id":"2302.14326","title":"Ethical Frameworks and Computer Security Trolley Problems: Foundations for Conversations","created":"2023-02-28","cited_by_count":11},{"id":"2408.11749","title":"Against All Odds: Overcoming Typology, Script, and Language Confusion in Multilingual Embedding Inversion Attacks","created":"2024-08-21","cited_by_count":10},{"id":"2204.14017","title":"Backdoor Attacks in Federated Learning by Rare Embeddings and Gradient Ensembling","created":"2022-04-29","cited_by_count":9},{"id":"2405.16656","title":"\"I Searched for a Religious Song in Amharic and Got Sexual Content Instead\": Investigating Online Harm in Low-Resourced Languages on YouTube","created":"2024-05-26","cited_by_count":9},{"id":"2204.10192","title":"Residue-Based Natural Language Adversarial Attack Detection","created":"2022-04-17","cited_by_count":8},{"id":"2210.06894","title":"Dim-Krum: Backdoor-Resistant Federated Learning for NLP with Dimension-wise Krum-Based Aggregation","created":"2022-10-13","cited_by_count":8},{"id":"2402.12026","title":"Acquiring Clean Language Models from Backdoor Poisoned Datasets by Downscaling Frequency Space","created":"2024-02-19","cited_by_count":8},{"id":"2404.02356","title":"Two Heads are Better than One: Nested PoE for Robust Defense Against Multi-Backdoors","created":"2024-04-02","cited_by_count":7},{"id":"2306.13789","title":"Deconstructing Classifiers: Towards A Data Reconstruction Attack Against Text Classification Models","created":"2023-06-23","cited_by_count":6},{"id":"2404.02393","title":"Backdoor Attack on Multilingual Machine Translation","created":"2024-04-03","cited_by_count":5},{"id":"2202.05758","title":"Using Random Perturbations to Mitigate Adversarial Attacks on Sentiment Analysis Models","created":"2022-02-11","cited_by_count":4},{"id":"2412.02323","title":"Pay Attention to the Robustness of Chinese Minority Language Models! Syllable-level Textual Adversarial Attack on Tibetan Script","created":"2024-12-03","cited_by_count":4},{"id":"2402.03477","title":"Arabic Synonym BERT-based Adversarial Examples for Text Classification","created":"2024-02-05","cited_by_count":3},{"id":"2403.16432","title":"$\\textit{LinkPrompt}$: Natural and Universal Adversarial Attacks on Prompt-based Language Models","created":"2024-03-25","cited_by_count":3},{"id":"2203.02958","title":"Towards a Responsible AI Development Lifecycle: Lessons From Information Security","created":"2022-03-06","cited_by_count":2},{"id":"2311.08598","title":"$DA^3$: A Distribution-Aware Adversarial Attack against Language Models","created":"2023-11-14","cited_by_count":1}],"next_cursor":null}